11.4. Iterating over lines in a file¶

Recall the contents of the qbdata.txt file.

Colt McCoy QB CLE  135 222 1576    6   9   60.8%   74.5
Josh Freeman QB TB 291 474 3451    25  6   61.4%   95.9
Michael Vick QB PHI    233 372 3018    21  6   62.6%   100.2
Matt Schaub QB HOU 365 574 4370    24  12  63.6%   92.0
Philip Rivers QB SD    357 541 4710    30  13  66.0%   101.8
Matt Hasselbeck QB SEA 266 444 3001    12  17  59.9%   73.2
Jimmy Clausen QB CAR   157 299 1558    3   9   52.5%   58.4
Joe Flacco QB BAL  306 489 3622    25  10  62.6%   93.6
Kyle Orton QB DEN  293 498 3653    20  9   58.8%   87.5
Jason Campbell QB OAK  194 329 2387    13  8   59.0%   84.5
Peyton Manning QB IND  450 679 4700    33  17  66.3%   91.9
Drew Brees QB NO   448 658 4620    33  22  68.1%   90.9
Matt Ryan QB ATL   357 571 3705    28  9   62.5%   91.0
Matt Cassel QB KC  262 450 3116    27  7   58.2%   93.0
Mark Sanchez QB NYJ    278 507 3291    17  13  54.8%   75.3
Brett Favre QB MIN 217 358 2509    11  19  60.6%   69.9
David Garrard QB JAC   236 366 2734    23  15  64.5%   90.8
Eli Manning QB NYG 339 539 4002    31  25  62.9%   85.3
Carson Palmer QB CIN   362 586 3970    26  20  61.8%   82.4
Alex Smith QB SF   204 342 2370    14  10  59.6%   82.1
Chad Henne QB MIA  301 490 3301    15  19  61.4%   75.4
Tony Romo QB DAL   148 213 1605    11  7   69.5%   94.9
Jay Cutler QB CHI  261 432 3274    23  16  60.4%   86.3
Jon Kitna QB DAL   209 318 2365    16  12  65.7%   88.9
Tom Brady QB NE    324 492 3900    36  4   65.9%   111.0
Ben Roethlisberger QB PIT  240 389 3200    17  5   61.7%   97.0
Kerry Collins QB TEN   160 278 1823    14  8   57.6%   82.2
Derek Anderson QB ARI  169 327 2065    7   10  51.7%   65.9
Ryan Fitzpatrick QB BUF    255 441 3000    23  15  57.8%   81.8
Donovan McNabb QB WAS  275 472 3377    14  15  58.3%   77.1
Kevin Kolb QB PHI  115 189 1197    7   7   60.8%   76.1
Aaron Rodgers QB GB    312 475 3922    28  11  65.7%   101.2
Sam Bradford QB STL    354 590 3512    18  15  60.0%   76.5
Shaun Hill QB DET  257 416 2686    16  12  61.8%   81.3

We will now use this file as input in a program that will do some data processing. In the program, we will read each line of the file and print it with some additional text. Because text files are sequences of lines of text, we can use the for loop to iterate through each line of the file.

A line of a file is defined to be a sequence of characters up to and including a special character called the newline character. If you evaluate a string that contains a newline character you will see the character represented as \n. If you print a string that contains a newline you will not see the \n, you will just see its effects. When you are typing a Python program and you press the enter or return key on your keyboard, the editor inserts a newline character into your text at that point.

As the for loop iterates through each line of the file the loop variable will contain the current line of the file as a string of characters. The general pattern for processing each line of a text file is as follows:

for line in myFile:
    statement1
    statement2
    ...

To process all of our quarterback data, we will use a for loop to iterate over the lines of the file. Using the split method, we can break each line into a list containing all the fields of interest about the quarterback. We can then take the values corresponding to first name, lastname, and passer rating to construct a simple sentence.